Statistical Foundations

A strong grasp of classical statistics is essential, particularly for roles that involve experimental design, risk modeling, or working alongside actuarial teams.

Be ready to go over:

• Hypothesis Testing – Formulating hypotheses, determining sample sizes, and interpreting statistical significance in business experiments.
• Probability Distributions – Understanding which distributions model real-world phenomena (e.g., Poisson for claim frequency, Lognormal for claim severity).
• Regression Diagnostics – Checking model assumptions (homoscedasticity, normality of residuals) and correcting violations.

Example questions or scenarios:

• "How would you design an A/B test to evaluate a new risk-scoring algorithm, and how would you determine when you have collected enough data to make a statistically sound decision?"
• "Explain the difference between parametric and non-parametric statistical methods, and provide an example of when you must use a non-parametric approach."

As a Data Scientist at Starr Companies, your daily responsibilities will span the entire machine learning lifecycle, from initial data exploration and business alignment to model deployment and monitoring.

Your primary responsibilities will include:

• Designing, developing, and deploying end-to-end predictive models and machine learning algorithms to solve complex business problems across underwriting, claims, and operations.
• Collaborating closely with domain experts, including underwriters and actuaries, to understand business requirements, identify data opportunities, and translate business challenges into analytical frameworks.
• Querying, cleaning, and preprocessing large, complex, and often unstructured datasets from multiple internal and external data sources to build robust modeling pipelines.
• Performing rigorous statistical analysis and feature engineering to uncover predictive signals and optimize model performance.
• Communicating complex analytical findings, model methodologies, and key insights to both technical and non-technical stakeholders, including senior leadership.
• Monitoring deployed models to track performance, identify data drift, and perform necessary retraining or model updates to ensure ongoing accuracy and business value.

To be competitive for the Data Scientist position at Starr Companies, you must present a strong combination of technical proficiency, academic or professional preparation, and strong communication skills.

• Must-have skills – Strong proficiency in Python or R, with deep experience in SQL for data extraction and manipulation. Solid understanding of core machine learning libraries (such as scikit-learn, XGBoost, or LightGBM) and a proven track record of building and validating predictive models (such as logistic regression, random forests, and gradient boosting).
• Nice-to-have skills – Prior experience in the insurance, reinsurance, or financial services industries, particularly working with actuarial or underwriting data. Familiarity with cloud platforms (AWS, Azure, or GCP) and experience deploying machine learning models into production environments. Knowledge of advanced statistical techniques like survival analysis or Bayesian modeling.
• Experience level – Typically requires a Master’s or Ph.D. in a quantitative field (such as Data Science, Statistics, Computer Science, Economics, or Engineering) or a Bachelor’s degree with equivalent, highly relevant professional experience. Candidates should demonstrate several years of hands-on experience applying machine learning to real-world datasets.
• Soft skills – Exceptional communication and presentation skills, with a demonstrated ability to explain complex technical concepts clearly to non-technical business partners. Strong problem-solving capabilities, adaptability, and a highly collaborative mindset.

Q: How technical is the initial Hirevue video interview? A: It is highly technical. Unlike many companies that use the initial video screen for basic behavioral or background questions, Starr Companies uses this stage to evaluate core machine learning concepts, statistical theory, and scenario-based problem-solving. You should prepare for this round with the same rigor as you would a live technical interview.

Q: What is the typical timeline from the initial application to an offer? A: The timeline can vary depending on the specific team and location, but candidates often report a multi-week process. Because the process involves multiple stages—including automated screens, live technical panels, and comprehensive loops—it can take anywhere from 4 to 8 weeks to complete.

Q: How does the data science team collaborate with other departments at Starr Companies? A: Data scientists work in a highly collaborative environment, partnering closely with actuaries, underwriters, and product managers. A key part of the role is translating complex statistical models into practical tools that business teams can use to make better risk-selection and pricing decisions.

Q: What is the remote or hybrid work policy for Data Scientists? A: Starr Companies generally operates on a hybrid model, requiring some days in the office depending on the specific team and office location (such as Dallas, Atlanta, or Chicago). It is best to clarify the exact hybrid expectations with your recruiter during the initial stages of the process.

To maximize your chances of success when interviewing for a Data Scientist position at Starr Companies, keep these practical, insider tips in mind:

• Master the "Why" Behind the "How": When walking through your technical decisions, do not just describe the steps you took. Explain why you chose a specific algorithm, why you selected a particular evaluation metric, and why that choice makes sense for the business problem.
• Prepare for Scenario-Based Questions: Be ready to discuss how you would handle messy, real-world data constraints. Practice structuring answers for scenarios involving high-dimensional data, missing values, and highly imbalanced datasets.
• Practice Clear, Structured Explanations: Because the initial screen is an automated video interview, practice speaking clearly and structuring your thoughts under time pressure. Use frameworks like STAR (Situation, Task, Action, Result) to keep your answers concise and impactful.
• Show Interest in the Domain: Familiarize yourself with basic insurance concepts, such as risk selection, underwriting, and loss ratios. Demonstrating that you understand how your models will be applied to solve real business challenges will set you apart from other candidates.

The Data Scientist role at Starr Companies represents an exceptional opportunity to apply advanced machine learning and predictive modeling to high-impact, real-world financial and insurance challenges. The work is intellectually stimulating, technically rigorous, and directly influences the company's global underwriting and risk-management strategies. Joining this team means your models will actively drive strategic business outcomes at scale.

To succeed in this competitive interview process, focus your preparation on mastering core machine learning algorithms, refining your statistical foundations, and practicing your ability to articulate complex technical decisions clearly. Treat the initial automated video screen with the same level of preparation as a final-round technical panel, and ensure you can seamlessly bridge the gap between deep technical details and high-level business impact.

The compensation data above reflects the competitive salary ranges offered for the Data Scientist position. When evaluating an offer, keep in mind that total compensation at Starr Companies typically includes a base salary paired with performance-based bonuses and comprehensive benefits. Your specific offer will depend on your experience level, technical expertise, and geographic location.

With a structured approach to your preparation and a clear focus on demonstrating both technical depth and business value, you can confidently navigate the interview process. For additional resources, community insights, and detailed interview preparation guides, explore the wealth of information available on Dataford to help you put your best foot forward.